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Julia Lust
Julia Lust
Robert Bosch GmbH
Bestätigte E-Mail-Adresse bei de.bosch.com
Titel
Zitiert von
Zitiert von
Jahr
GraN: an efficient gradient-norm based detector for adversarial and misclassified examples
J Lust, AP Condurache
ESANN 2020 (European Symposium on Artificial Neural Networks, Computational …, 2020
322020
A Survey on Assessing the Generalization Envelope of Deep Neural Networks: Predictive Uncertainty, Out-of-distribution and Adversarial Samples
J Lust, AP Condurache
CoRR, 2020
15*2020
Efficient detection of adversarial, out-of-distribution and other misclassified samples
J Lust, AP Condurache
Neurocomputing 470, 335-343, 2022
102022
Shortest cable routing in offshore wind farms
J Lust
Bachelor thesis, RWTH Aachen University, 2016
22016
GIT: Detecting Uncertainty, Out-Of-Distribution and Adversarial Samples using Gradients and Invariance Transformations
J Lust, AP Condurache
2023 International Joint Conference on Neural Networks (IJCNN), 1-10, 2023
2023
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